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AI Applied to Cancer

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning

British Journal of Cancer volume 125, pages 351–357 (2021)Cite this article

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Abstract

Background

Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients.

Methods

A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation.

Results

Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism.

Conclusions

We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

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Fig. 1: Study design of lipidomic analysis.
Fig. 2: Overview of untargeted serum lipidomic analysis in the exploratory study.
Fig. 3: SVM-based diagnostic modelling and performance evaluation in the validation study.
Fig. 4: Integrative analysis of transcriptomic and lipidomic data of ESCC patients compared to normal controls reveals potential mechanisms of lipid dysregulation.

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Acknowledgements

We thank Dr. Z. Zhou for the analysis of transcriptomic data.

Author information

Author notes

  1. These authors contributed equally: Yuyao Yuan, Zitong Zhao, Liyan Xue.

Authors and Affiliations

  1. Department of Pathology, School of Basic Medical Sciences, Institute of Systems Biomedicine, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China

    Yuyao Yuan, Guangxi Wang, Huajie Song, Ruifang Pang, Juntuo Zhou & Yuxin Yin

  2. State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Zitong Zhao & Yongmei Song

  3. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Liyan Xue

  4. Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen, China

    Ruifang Pang & Yuxin Yin

  5. Department of Medical Genetics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China

    Jianyuan Luo

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Contributions

Y. Yin. and Y.S. designed this study. Y. Yuan, H.S., G.W., J.Z. and R.P. conducted sample preparation and LC-MS analysis. Z.Z., L.X. and Y.S. collected clinical samples and clinical information and generated transcriptomic data. Y. Yuan and G.W. conducted data analysis and interpreted the data with J.Z., R.P. and L.J. Y. Yuan, Y.S. and Y. Yin wrote the manuscript.

Corresponding authors

Correspondence to Yongmei Song or Yuxin Yin.

Ethics declarations

Ethics approval and consent to participate

This study was performed in accordance with the Declaration of Helsinki and was conducted after obtaining approval from the Independent Ethics Committee at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (No. NCC2596). Informed consent was obtained from all patients or healthy controls. All methods were performed in accordance with relevant guidelines and regulations.

Consent to publish

Not applicable.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Funding information

This work was supported by grants to Y. Yin, including the National Key Research and Development Programme of China (2016YFA0500302 to Y. Yin), the National Natural Scientific Foundation of China (31420103905, 81321003, 81430056 and 81372491 to Y. Yin), the Beijing Natural Science Foundation (Key grant 7161007 to Y. Yin), the Shu Fan Education and Research Foundation (to Y. Yin), and Lam Chung Nin Foundation for Systems Biomedicine (to Y. Yin).

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Yuan, Y., Zhao, Z., Xue, L. et al. Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning. Br J Cancer 125, 351–357 (2021). https://doi.org/10.1038/s41416-021-01395-w

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  • DOI: https://doi.org/10.1038/s41416-021-01395-w

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